{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6a8f8549",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Leveraging LLMs for Real-Time Decision Making in Calamities: Analyzing the HarveyTweet Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c377221b-76f4-477e-a5d4-96c3c22fe34b",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Table of Contents"
   ]
  },
  {
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   "id": "2c637307-8a61-47bd-bb95-ae966ddf1811",
   "metadata": {
    "tags": []
   },
   "source": [
    "* [Introduction](#introduction)\n",
    "* [Brief Introduction to Toolbox and Python API](#brief-introduction-to-toolbox-and-python-api)\n",
    "* [Introduction to workflow](#introduction-to-workflow)\n",
    "* [Imports](#imports)\n",
    "* [Prompt Engineering](#prompt-engineering)\n",
    "* [Esri Model Definition(EMD) file](#esri-model-definition-emd-file)\n",
    "* [Anatomy of the Extension Function](#anatomy-of-the-extension-function)\n",
    "  * [Define the ```__init__``` function](#define-the-__init__-function)\n",
    "  * [Define the ```getParameterInfo``` function](#define-the-getparameterinfo-function)\n",
    "  * [Define the ```initialize``` function](#define-the-initialize-function)\n",
    "  * [Define the ```getConfiguration``` function](#define-the-getconfiguration-function)\n",
    "  * [Define the ```predict``` function](#define-the-predict-function)\n",
    "* [Complete NLP Function](#complete-nlp-function)\n",
    "* [Create Custom ESRI Deep Learning Package (.dlpk)](#custom-esri-deep-learning-package-dlpk-file)\n",
    "* [ESRI Deep Learning Package (.dlpk) in arcgis.learn API](#using-the-custom-esri-deep-learning-package-dlpk-file-with-the-arcgislearn-api)\n",
    "* [Tool Interface](#tool-interface)\n",
    "* [Sample Input Table](#sample-input-table)\n",
    "* [Sample Output Table](#sample-output-table)\n",
    "* [Conclusion](#conclusion)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8a7b212-1558-4255-9b09-f551e7a09c65",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0cbbb0a-b4ce-4205-87c9-3aac6bc9510e",
   "metadata": {},
   "source": [
    "### Brief Introduction to Toolbox and Python API"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecfd75a6-205f-423e-b197-d60b1bb4bdfd",
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   "source": [
    "The Text Analysis toolset in the **GeoAI toolbox** provides a set of tools for various text processing tasks, including _text classification, entity extraction, and text translation_. The natural language processing (NLP) models created using these tools are built on language model backbones like BERT, RoBERTa, T5 and large language models (LLMs) Mistral, to ensure high-performance text analysis.\n",
    "\n",
    "However, what if you come across a **Text model** or **LLM** that is not yet a part of the learn module, and you want to use it from its library or its open-source code repository? What if you have tune your own **text model** for a task such as standardizing addresses, conducting sentiment analysis, or extracting custom entities or relationships not currently supported by the Text Analysis toolset? How would you use it inside the **ArcGIS Pro**?\n",
    "\n",
    "To meet these needs, you can integrate ArcGIS with external third-party language models. This includes open source LLMs as well as cloud-hosted, commercial LLMs accessed using a web API. Keep in mind that if you are using a web hosted LLM, the data that you are processing will be sent to the LLM provider for processing. Python developers can author a custom NLP function to integrate with external models and package their model as an ESRI Deep Learning Package (.dlpk) file for use with the following tools:\n",
    "\n",
    "1. [Classify Text Using Deep Learning (GeoAI)](https://pro.arcgis.com/en/pro-app/latest/tool-reference/geoai/classify-text-using-deep-learning.htm)\n",
    "2. [Extract Entities Using Deep Learning (GeoAI)](https://pro.arcgis.com/en/pro-app/latest/tool-reference/geoai/extract-entities-using-deep-learning.htm)\n",
    "3. [Transform Text Using Deep Learning (GeoAI)](https://pro.arcgis.com/en/pro-app/latest/tool-reference/geoai/transform-text-using-deep-learning.htm)\n",
    "\n",
    "ESRI Deep Learning Package (.dlpk) file of the custom NLP function is also supported using `arcgis.learn` Python API. It supports third party custom models through various classes, including `TextClassifier`, `EntityRecognizer`, and `SequenceToSequence`.\n",
    "\n",
    "\n",
    "This document presents a use case where a hosted commercial LLM has been utilized to perform an address standardization task. It provides a step-by-step guide detailing the various components of the **NLP Function** and explains how to create a ESRI Deep Learning Package (.dlpk) file using the NLP function. We will also achieve the same task using the **Python API**.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01ad0ec7-2fee-43ba-b9da-60f0e0058520",
   "metadata": {},
   "source": [
    "### Introduction to workflow"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47176560-f8da-4ce6-8243-14e15cd79305",
   "metadata": {},
   "source": [
    "This project aims to showcase the capabilities of **Large Language Models (LLMs)** for making informed decisions during emergencies. While traditional approaches often involve post-hoc data analysis, our focus is on leveraging live data streaming and near-real-time analysis to deliver targeted relief to affected individuals. \n",
    "\n",
    " We utilized a dataset of 1,000 annotated tweets, categorized into various classes such as street addresses, local organizations, and more. By employing **On-Prem Llama-3**, we were able to identify locations and classify the tweets into relevant categories such as **Public service, Health, and others**. This work also introduces the concept of **model extension** within the Text Tools and API.\n",
    "\n",
    " **Intended Audience:**  \n",
    "  Analysts and Extension Model Developers\n",
    "\n",
    " **Dataset:**  \n",
    "  [HarveyTweet Dataset](https://arxiv.org/pdf/2009.12914)\n",
    "\n",
    " **Backbone:**  \n",
    "  Llama-3\n",
    "\n",
    " **Capabilities Demonstrated:**  \n",
    "  - Prompt Engineering Techniques\n",
    "  - Out-of-the-Box LLM Utilization\n",
    "  - Minimal Annotation Requirements\n",
    "  - Comparable Accuracy to Traditional Methods\n",
    "  - Integration of Custom Models within ArcGIS Pro's Text Tool and API\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b33c40bc-1ee1-497c-8789-2d5aa6eb8b8c",
   "metadata": {
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   },
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
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   "id": "a5e30912-b73c-484e-9b56-19aa4841d56d",
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   "source": [
    "import re\n",
    "import json\n",
    "from string import Template\n",
    "from arcgis.features import FeatureSet\n",
    "from transformers import AutoModelForCausalLM, AutoTokenizer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1194b485-3a2c-45a1-a095-f43e813bbe4a",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
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   },
   "source": [
    "## Prompt Engineering"
   ]
  },
  {
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   "id": "7a2ee844-4a5f-4cb5-ae6b-8fa0b79ce913",
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   "source": [
    "To execute the specified task—identifying key information from tweets—we will design a **system prompt** to guide the model’s overall behavior and set necessary guardrails. Additionally, a **user prompt** will be implemented to define the task with greater precision."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b58b4f8-604a-4d82-b37d-35f91d12a1a9",
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   "source": [
    "\n",
    "SYSTEM_PROMPT = \"\"\"\n",
    "You are a Text classifier. You need to tag in following classes $classes. Your output must be devoid of any biases.\n",
    "You must not generate any explanation or notes for your response. Just generated the class which you thing best fit for the tweets.  \n",
    "Your output must be devoid of any biases.\n",
    "        \n",
    "        You must adhere the below JSON schema while generating the output. Always place your output between << and >>. If there are multiple classes, then place it as <<label1, label2>>. \n",
    "        \n",
    "        This is the representative example: \n",
    "        \"input\": $sentence\n",
    "        \"output\": $answer\n",
    "        $user_prompt\n",
    "\"\"\"\n",
    "\n",
    "USER_PROMPT = \"\"\"\n",
    "Classify all the input sentences from the text field into the classes present in the label field. Don't make up your own labels. You need to classify into single class.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "993cd22e-aee2-40ac-b9b3-530dd92a101d",
   "metadata": {
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   },
   "source": [
    "## Esri Model Definition(EMD) file "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6aa80d78-407c-499f-b4b3-76a11f3bc6db",
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   "source": [
    "The ESRI Model Definition (.emd) file will include both required and optional keys to facilitate model execution. To run the **Classify Text Using Deep Learning (GeoAI)** Tool, we need to supply `InferenceFunction`, `ModelType` and `OutputField`. \n",
    "\n",
    "1. `InferenceFunction`: Name of the module that contains the definition of NLP function\n",
    "2. `ModelType`: Defines the type of task. For the **Classify Text Using Deep Learning (GeoAI)** Tool, it will be  `TextClassifier`\n",
    "3. `OutputField`: Name of the field that contains the output\n",
    "\n",
    "Other keys are the prerogative of the Model extension author. In this case, we stated the `few-shot prompting` related information - `examples` and `prompt`. We will utilize this information to construct a clear and effective prompt for the task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d5af81e-c6f6-4504-84ea-66f82f9bfbdf",
   "metadata": {
    "vscode": {
     "languageId": "json"
    }
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   "source": [
    "{\n",
    "    \"InferenceFunction\": \"MyTextClassifier.py\",\n",
    "    \"Label\": [\n",
    "      \"Health emergency\",\n",
    "      \"Public service failures\",\n",
    "      \"Others\"\n",
    "    ],\n",
    "    \"prompt\": \"Classify all the input sentences from the text field into the classes present in the label field. Don't make up your own labels. You need to classify into single class.\",\n",
    "    \"examples\": [\n",
    "      [\n",
    "        \"Texas Harvey - We need mandatory evacuation for Pecan Grove .\",\n",
    "        \"Health emergency\"\n",
    "      ],\n",
    "      [\n",
    "        \"Rockport about to get hit by 130 mph winds after several building collapsed , many injured , and high school partial â€¦\",\n",
    "        \"Health emergency\"\n",
    "      ],\n",
    "      [\n",
    "        \"* * * PLS SHARE : Houston police need socks , undershirts , underwear at 1602 State St ( Union Hall ) Harvey HoustonFlood\",\n",
    "        \"Public service failuers\"\n",
    "      ],\n",
    "      [\n",
    "        \"1253mm of rain recorded SE of Houston as a result of HurricaneHarvey . To put that into perspective , the ANNUAL ave â€¦\",\n",
    "        \"Others\"\n",
    "      ]\n",
    "    ],\n",
    "    \"ModelType\": \"TextClassifier\",\n",
    "    \"ModelName\": \"TextClassifier\",\n",
    "    \"OutputField\": \"class_3\"\n",
    "  }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89cfcfb7-8c15-404f-9336-9057c72ee5e1",
   "metadata": {
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   },
   "source": [
    "## Anatomy of the Extension Function"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccc1cbc9-d4dd-46bd-b6ad-578a0aa6d797",
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   "source": [
    "Model extension requires the process be wrapped in a class with the following functions implemented:\n",
    "\n",
    "- `__init__`\n",
    "\n",
    "- `getParameterInfo`\n",
    "\n",
    "- `initialize`\n",
    "\n",
    "- `getConfiguration`\n",
    "\n",
    "- `predict`\n",
    "\n",
    "Let’s create a custom extension model to classify input tweets into the categories: **Health Emergency, Public Service Failures, and Other**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0db98714-0c8a-4e22-a806-6ba8aa553a40",
   "metadata": {},
   "source": [
    "### Define the ```__init__``` function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "837160b7-2a7d-4e3f-88f5-56a5508ca44c",
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   "source": [
    "def __init__(self, **kwargs):\n",
    "    self.description = \"Tweet classification into pre-defined classes using Llama-3 model\"\n",
    "    self.name = \"Tweet classifier for hurricance harvey\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34845994-8622-4831-b44d-e36276030a5d",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
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   },
   "source": [
    "### Define the ```getParameterInfo``` function "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9aa02e1-fc97-428e-908e-c271b33c5670",
   "metadata": {},
   "source": [
    "This function gathers parameters from the user for the **Classify Text Using Deep Learning (GeoAI)** Tool. The demo utilizes Llama-3 and employs the `transformers` library for loading and inference. Users can customize the model’s input and output by specifying generation parameters, including `max_length` and `temperature`, to optimize the classification process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "999eb0cf",
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   "outputs": [],
   "source": [
    "def getParameterInfo(self):\n",
    "    t = [\n",
    "        {\n",
    "            \"name\": \"max_token\",\n",
    "            \"dataType\": \"integer\",\n",
    "            \"required\": False,\n",
    "            \"displayName\": \"Maximum number of tokens\",\n",
    "            \"description\": \"Maximum number of tokens\",\n",
    "            \"value\": 512\n",
    "\n",
    "        },\n",
    "        {\n",
    "            \"name\": \"temperature\",\n",
    "            \"dataType\": \"float\",\n",
    "            \"required\": False,\n",
    "            \"displayName\": \"temperature\",\n",
    "            \"description\": \"This parameter will control the generation value; ideally, it should be kept within the range of 0 to 1\",\n",
    "            \"value\": 0.1\n",
    "\n",
    "        },\n",
    "    ]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad320582-1ed2-414b-b567-128f3ac332dc",
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   "source": [
    "### Define the ```initialize``` function"
   ]
  },
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   "source": [
    "The `initialize` function acts as the central hub for the extension. Within this function, we will set up the necessary variables. It accepts two parameters via `kwargs`:\n",
    "\n",
    "#### Parameters in `kwargs`\n",
    "- **`model`**: The path to the EMD file.\n",
    "- **`device`**: The name of the device (either GPU or CPU), which is particularly important for on-premises models.\n",
    "\n",
    "Unlike the `__init__` method, which creates an instance of the NLP function, `initialize` reads the EMD file and configures the essential variables needed for inference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd943109",
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   "outputs": [],
   "source": [
    "def initialize(self, **kwargs):\n",
    "    if not 'model' in kwargs:\n",
    "        return\n",
    "\n",
    "    with open(kwargs[\"model\"]) as f:\n",
    "        temp_payload = json.load(f)\n",
    "\n",
    "    kwargs.update(temp_payload)\n",
    "    self.max_token = kwargs.get(\"max_token\", 1024)\n",
    "    self.temperature = kwargs.get(\"temperature\", 0.1)\n",
    "    self.prompt = kwargs.get(\"prompt\", None)\n",
    "    self.examples = kwargs.get(\"examples\", [])\n",
    "    self.labels = kwargs.get(\"Label\", [])\n",
    "    self._device = kwargs.get('device', \"cuda\")\n",
    "    if self._device == 'GPU':\n",
    "        self._device = \"cuda\"\n",
    "    model_path = \"meta-llama/Meta-Llama-3-8B-Instruct\"\n",
    "    self._llm_model = (\n",
    "        AutoModelForCausalLM.from_pretrained(model_path, token=\"hf_SECRET_KEY\")\n",
    "        .half()\n",
    "        .to(self._device)\n",
    "        .eval()\n",
    "    )\n",
    "    self._tokenizer = AutoTokenizer.from_pretrained(model_path, token=\"hf_SECRET_KEY\",\n",
    "                                                    use_fast=False, padding_side='left')\n",
    "    self._tokenizer.pad_token_id = self._tokenizer.eos_token_id"
   ]
  },
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   "cell_type": "markdown",
   "id": "7d121c9d-e10e-4bbb-96db-402c34128e10",
   "metadata": {
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   },
   "source": [
    "### Define the ```getConfiguration``` function "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85296efe-2889-466f-a985-82c9353d0065",
   "metadata": {},
   "source": [
    "This function receives `kwargs` that contain parameter names and values collected from the user. Additionally, it includes parameters like `batch_size`, which controls the tool's behavior. The Text Transform Tool supports customization of `batch_size`, determining how many records are processed in a single batch. This parameter plays a crucial role in optimizing the tool's performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6687993f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def getConfiguration(self, **kwargs):\n",
    "    self.max_token = kwargs.get(\"max_token\", 1024)\n",
    "    try:\n",
    "        self.max_token = float(self.max_token)\n",
    "        if self.max_token > 1024:\n",
    "            self.max_token = 512\n",
    "    except:\n",
    "        self.max_token = 512\n",
    "    # Edit the batch size\n",
    "    if kwargs[\"batch_size\"] > 4:\n",
    "        kwargs[\"batch_size\"] = 4\n",
    "\n",
    "    self.temperature = kwargs.get(\"temperature\", 0.1)\n",
    "    try:\n",
    "        self.temperature = float(self.temperature)\n",
    "    except:\n",
    "        self.temperature = 0.1\n",
    "\n",
    "    return kwargs"
   ]
  },
  {
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   "id": "58d9f9d5",
   "metadata": {
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   },
   "source": [
    "### Build the Prompt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc25522c",
   "metadata": {},
   "source": [
    "We will construct the prompt by combining the `system_prompt` and `user_prompt`. First, we will configure the `system_prompt` using the `Template` class from the `string` library to make it parametric. After setting up the system prompt, we will use examples to create a structure for few-shot prompting. Once this process is complete, we will have a finalized prompt ready for downstream tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23d84b4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def format_prompt(self):\n",
    "    # add the labels. Classes for the classification\n",
    "    global SYSTEM_PROMPT\n",
    "    payload = {\n",
    "        \"user_prompt\": self.prompt,\n",
    "        \"sentence\": self.examples[0][0],\n",
    "        \"answer\": f\"<<{self.examples[0][1]}>>\",\n",
    "        \"classes\": \",\".join(self.labels),\n",
    "    }\n",
    "    SYSTEM_PROMPT = Template(SYSTEM_PROMPT).substitute(**payload)\n",
    "    self.prompt = self.prompt + f\"\\n\\nClass labels are specified below:\\n{','.join(self.labels)}. Your out should select from provided labels.\"\n",
    "    system_prompt = f\"{SYSTEM_PROMPT}\\n\\n{self.prompt}\\n\\n{end_seq}\"\n",
    "    self.prompt = [{\"role\": \"system\", \"content\": system_prompt}]\n",
    "    for idx, i in enumerate(self.examples):\n",
    "        if idx == 0:\n",
    "            pass\n",
    "        else:\n",
    "            self.prompt += [{\"role\": \"user\", \"content\": i[0]}]\n",
    "            self.prompt += [\n",
    "                {\n",
    "                    \"role\": \"assistant\",\n",
    "                    \"content\": f\"<<{i[1]}>>\",\n",
    "                }\n",
    "            ]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93e54e4f-4460-4215-8170-fabf551bc9a2",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Define the ```predict``` function "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a5196c4-e58f-42df-aa0e-1a00b8370d45",
   "metadata": {},
   "source": [
    "This function serves as the entry point for performing inference on the input. It takes a `FeatureSet` as input along with the field name that contains the relevant data. First, we will retrieve the field name from the provided `kwargs` and use it to extract the list of sentences for inference.\n",
    "\n",
    "In the next step, we will create the payload for Llama-3 by preparing the input in the specified format, consisting of a sequence of `system message` and `user message`. The formatted message will then be tokenized and passed to the model. We will use the `generate` method to obtain the model's response, which will be decoded using the `batch_decode` function. Since the output is enclosed within `<<` and `>>`, we will use a regular expression to parse it. \n",
    "\n",
    "The parsed output will then be converted into a `FeatureSet`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07728e8b-bc5c-4892-8c88-db2de7b01358",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def predict(self, feature_set: FeatureSet, **kwargs):\n",
    "    field = kwargs[\"input_field\"]\n",
    "    text_list = feature_set.df[field].to_list()\n",
    "    payload = []\n",
    "    \n",
    "    # Build the payload for a single batch \n",
    "    for message in text_list:\n",
    "        temp_val = self.prompt + [{\"role\": \"user\", \"content\": message}]\n",
    "        payload.append(\n",
    "                self._tokenizer.apply_chat_template(temp_val, tokenize=False, add_generation_prompt=True)\n",
    "        )\n",
    "    batch_token = self._tokenizer(\n",
    "        payload, return_tensors=\"pt\", padding=True, max_len=self.max_token\n",
    "    ).to(self._device) # tokenize the input\n",
    "    \n",
    "    # generate the inference\n",
    "    resps = self._llm_model.generate(\n",
    "        **batch_token,\n",
    "        max_new_tokens=2048,\n",
    "        do_sample=True,\n",
    "        pad_token_id=self._tokenizer.eos_token_id,\n",
    "        temperature=self.temperature\n",
    "    )\n",
    "\n",
    "    return_val = []\n",
    "    for idx, generated_ids in enumerate(resps):\n",
    "        sliced_response = self._tokenizer.batch_decode(\n",
    "            [generated_ids[len(batch_token[\"input_ids\"][idx]): -1]],\n",
    "            skip_special_tokens=True,\n",
    "        )[0]\n",
    "        labels =  re.findall(\"(?<=<<)(.*?)(?=>>)\", sliced_response)\n",
    "        if len(labels):\n",
    "            return_val.append(labels[0])\n",
    "        else:\n",
    "            return_val.append(\"Others\")\n",
    "\n",
    "    features_list = []\n",
    "    for i, j in zip(text_list, return_val):\n",
    "        features_list.append({'attributes': {'input_str': i, \"class_3\": j}})\n",
    "\n",
    "    feature_dict = {\n",
    "        \"fields\": [\n",
    "            {\"name\": \"input_str\", \"type\": \"esriFieldTypeString\"},\n",
    "            {\"name\": \"class_3\", \"type\": \"esriFieldTypeString\"},\n",
    "        ],\n",
    "        'geometryType': \"\",\n",
    "        'features': features_list\n",
    "    }\n",
    "    return FeatureSet.from_dict(feature_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1493d621-a9ef-4767-a730-76b0229a130d",
   "metadata": {},
   "source": [
    "Finally, the tool will receive a `FeatureSet` as output from the extensible function."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3949e3c3-5084-462b-99a6-b14574e04ec6",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Complete NLP Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "486d856c-6050-47a7-beee-15bc8e57d20c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import json\n",
    "from string import Template\n",
    "from arcgis.features import FeatureSet\n",
    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
    "\n",
    "\n",
    "\n",
    "SYSTEM_PROMPT = \"\"\"You are a Text classifier. You need to tag in following classes $classes. Your output must be devoid of any biases. You must not generate any explanation or notes for your response. Just generated the class which you thing best fit for the tweets.  Your output must be devoid of any biases.\"\n",
    "        \n",
    "        You must adhere the below JSON schema while generating the output. Always place your output between << and >>. If there is multiple class, then place it as <<label1, label2>>. \n",
    "        \n",
    "        This is the representative example: \n",
    "        \"input\": $sentence\n",
    "        \"output\": $answer\n",
    "        $user_prompt\n",
    "        \n",
    "        \"\"\"\n",
    "\n",
    "end_seq =  \"\"\n",
    "\n",
    "\n",
    "class MyTextClassifier:\n",
    "    def __init__(self, **kwargs):\n",
    "        self.description = \"Tweet classification into pre-defined classes using Llama-3 model.\"\n",
    "        self.name = \"Tweet classifier for hurricance harvey\"\n",
    "\n",
    "    def initialize(self, **kwargs):\n",
    "        if not 'model' in kwargs:\n",
    "            return\n",
    "\n",
    "        with open(kwargs[\"model\"]) as f:\n",
    "            temp_payload = json.load(f)\n",
    "\n",
    "        kwargs.update(temp_payload)\n",
    "        self.max_token = kwargs.get(\"max_token\", 1024)\n",
    "        self.temperature = kwargs.get(\"temperature\", 0.1)\n",
    "        self.prompt = kwargs.get(\"prompt\", None)\n",
    "        self.examples = kwargs.get(\"examples\", [])\n",
    "        self.labels = kwargs.get(\"Label\", [])\n",
    "        self._device = kwargs.get('device', \"cuda\")\n",
    "        if self._device == 'GPU':\n",
    "            self._device = \"cuda\"\n",
    "        model_path = \"meta-llama/Meta-Llama-3-8B-Instruct\"\n",
    "        self._llm_model = (\n",
    "            AutoModelForCausalLM.from_pretrained(model_path, token=\"hf_SECRET_KEY\")\n",
    "            .half()\n",
    "            .to(self._device)\n",
    "            .eval()\n",
    "        )\n",
    "        self._tokenizer = AutoTokenizer.from_pretrained(model_path, token=\"hf_SECRET_KEY\",\n",
    "                                                        use_fast=False, padding_side='left')\n",
    "        self._tokenizer.pad_token_id = self._tokenizer.eos_token_id\n",
    "\n",
    "    def format_prompt(self):\n",
    "        # Format the prompt using the examples and pre-defined classes.\n",
    "        global SYSTEM_PROMPT\n",
    "        payload = {\n",
    "            \"user_prompt\": self.prompt,\n",
    "            \"sentence\": self.examples[0][0],\n",
    "            \"answer\": f\"<<{self.examples[0][1]}>>\",\n",
    "            \"classes\": \",\".join(self.labels),\n",
    "        }\n",
    "        SYSTEM_PROMPT = Template(SYSTEM_PROMPT).substitute(**payload)\n",
    "        self.prompt = self.prompt + f\"\\n\\nClass labels are specified below:\\n{','.join(self.labels)}. Your out should select from provided labels.\"\n",
    "        system_prompt = f\"{SYSTEM_PROMPT}\\n\\n{self.prompt}\\n\\n{end_seq}\"\n",
    "        self.prompt = [{\"role\": \"system\", \"content\": system_prompt}]\n",
    "        for idx, i in enumerate(self.examples):\n",
    "            if idx == 0:\n",
    "                pass\n",
    "            else:\n",
    "                self.prompt += [{\"role\": \"user\", \"content\": i[0]}]\n",
    "                self.prompt += [\n",
    "                    {\n",
    "                        \"role\": \"assistant\",\n",
    "                        \"content\": f\"<<{i[1]}>>\",\n",
    "                    }\n",
    "                ]\n",
    "\n",
    "    def getParameterInfo(self):\n",
    "        t = [\n",
    "            {\n",
    "                \"name\": \"max_token\",\n",
    "                \"dataType\": \"integer\",\n",
    "                \"required\": False,\n",
    "                \"displayName\": \"Maximum number of tokens\",\n",
    "                \"description\": \"Maximum number of tokens\",\n",
    "                \"value\": 512\n",
    "\n",
    "            },\n",
    "            {\n",
    "                \"name\": \"temperature\",\n",
    "                \"dataType\": \"float\",\n",
    "                \"required\": False,\n",
    "                \"displayName\": \"temperature\",\n",
    "                \"description\": \"This parameter will control the generation value; ideally, it should be kept within the range of 0 to 1\",\n",
    "                \"value\": 0.1\n",
    "\n",
    "            },\n",
    "        ]\n",
    "        return t\n",
    "\n",
    "    def getConfiguration(self, **kwargs):\n",
    "        self.max_token = kwargs.get(\"max_token\", 1024)\n",
    "        try:\n",
    "            self.max_token = float(self.max_token)\n",
    "            if self.max_token > 1024:\n",
    "                self.max_token = 512\n",
    "        except:\n",
    "            self.max_token = 512\n",
    "        # Edit the batch size\n",
    "        if kwargs[\"batch_size\"] > 4:\n",
    "            kwargs[\"batch_size\"] = 4\n",
    "            \n",
    "        self.temperature = kwargs.get(\"temperature\", 0.1)\n",
    "        try:\n",
    "            self.temperature = float(self.temperature)\n",
    "        except:\n",
    "            self.temperature = 0.1\n",
    "\n",
    "        return kwargs\n",
    "\n",
    "    def predict(self, feature_set: FeatureSet, **kwargs):\n",
    "        field = kwargs[\"input_field\"]\n",
    "        text_list = feature_set.df[field].to_list()\n",
    "        payload = []\n",
    "\n",
    "        # Build the payload for a single batch\n",
    "        for message in text_list:\n",
    "            temp_val = self.prompt + [{\"role\": \"user\", \"content\": message}]\n",
    "            payload.append(\n",
    "                self._tokenizer.apply_chat_template(temp_val, tokenize=False, add_generation_prompt=True)\n",
    "            )\n",
    "        batch_token = self._tokenizer(\n",
    "            payload, return_tensors=\"pt\", padding=True, max_len=self.max_token\n",
    "        ).to(self._device)  # tokenize the input\n",
    "\n",
    "        # generate the inference\n",
    "        resps = self._llm_model.generate(\n",
    "            **batch_token,\n",
    "            max_new_tokens=2048,\n",
    "            do_sample=True,\n",
    "            pad_token_id=self._tokenizer.eos_token_id,\n",
    "            temperature=self.temperature\n",
    "        )\n",
    "\n",
    "        return_val = []\n",
    "        for idx, generated_ids in enumerate(resps):\n",
    "            sliced_response = self._tokenizer.batch_decode(\n",
    "                [generated_ids[len(batch_token[\"input_ids\"][idx]): -1]],\n",
    "                skip_special_tokens=True,\n",
    "            )[0]\n",
    "            labels = re.findall(\"(?<=<<)(.*?)(?=>>)\", sliced_response)\n",
    "            if len(labels):\n",
    "                return_val.append(labels[0])\n",
    "            else:\n",
    "                return_val.append(\"Others\")\n",
    "\n",
    "        features_list = []\n",
    "        for i, j in zip(text_list, return_val):\n",
    "            features_list.append({'attributes': {'input_str': i, \"class_3\": j}})\n",
    "\n",
    "        feature_dict = {\n",
    "            \"fields\": [\n",
    "                {\"name\": \"input_str\", \"type\": \"esriFieldTypeString\"},\n",
    "                {\"name\": \"class_3\", \"type\": \"esriFieldTypeString\"},\n",
    "            ],\n",
    "            'geometryType': \"\",\n",
    "            'features': features_list\n",
    "        }\n",
    "        return FeatureSet.from_dict(feature_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d4cac82-b182-4bab-8159-cba5cea70eeb",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Key Points:\n",
    "- All of the aforementioned functions must be encapsulated within a class.\n",
    "- The class name should match the Inference Function file name, excluding the file extension.\n",
    "- The author can define helper functions either within the class or in the module.\n",
    "- The author is responsible for handling exceptions when calling an endpoint.\n",
    "- The input field name must correspond to the name of the input field selected from the input table or feature layer."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c377e5d4-e6ec-4a8f-86f6-c8c8d4833e52",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Custom ESRI Deep Learning Package (.dlpk) file"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bfae9e3-9cd5-4608-a4d1-5ed6adbe6d36",
   "metadata": {},
   "source": [
    "To complete a custom NLP function setup, create an ESRI Deep Learning Package (.dlpk) file. \n",
    "\n",
    "Organize the files as follows:\n",
    "\n",
    "**Create a Folder**: Create a folder and include the custom NLP function file. In our case, we will create a folder `text_classification_llama`. Inside this, we will place `text_classification_llama.emd` and the NLP function file \n",
    "`MyTextClassifier.py`.\n",
    "\n",
    "  ```      \n",
    "text_classification_llama\n",
    "├── MyTextClassifier.py\n",
    "└── text_classification_llama.emd\n",
    "```\n",
    "\n",
    "**Zip the Folder**: Create a ZIP archive of the folder. Rename the .zip file to match the name of the ESRI Model Definition (.emd) file but with the .dlpk extension. The ESRI Deep Learning Package (.dlpk) file's name will be `text_classification_llama.dlpk`\n",
    "\n",
    "This ESRI Deep Learning Package (.dlpk) file is now ready for use with the `arcgis.learn` Python API and **Classify Text Using Deep Learning (GeoAI)** Tool."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3c5267a-8fcb-4e06-8d0a-634df21b0f63",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Using the Custom ESRI Deep Learning Package (.dlpk) file with the arcgis.learn API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5441ecdc-da8e-49d6-bfa6-c41167b4e722",
   "metadata": {},
   "outputs": [],
   "source": [
    "from arcgis.learn.text import TextClassifier\n",
    "\n",
    "# Initialize the model with the dlpk\n",
    "model = TextClassifier.from_model(r\"Path_to_the_dlpk\\text_classification_llama.dlpk\")\n",
    "\n",
    "# perform the inference\n",
    "model.predict([\"Rockport about to get hit by 130 mph winds after several building collapsed , many injured , and high school partial â€¦\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4b8d60b-84dd-4978-bfa8-7ddef51edffc",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Tool Interface"
   ]
  },
  {
   "attachments": {
    "64684b35-77dd-43bc-8592-ee9200b7b42b.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "02b2e742-133c-4f4e-b33f-0fc82a67b86b",
   "metadata": {},
   "source": [
    "![image.png](attachment:64684b35-77dd-43bc-8592-ee9200b7b42b.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "377d9ecf-ce85-4262-a676-9da8af1dfdd2",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Sample Input Table"
   ]
  },
  {
   "attachments": {
    "e5910582-6ec0-4308-9f2d-b30df159df23.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "63f452fd-08b7-49aa-b739-c08cdf126a36",
   "metadata": {},
   "source": [
    "![image.png](attachment:e5910582-6ec0-4308-9f2d-b30df159df23.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfa1bc42-0e9c-431c-a339-8efce9d4f3e3",
   "metadata": {},
   "source": [
    "### After Extension file loading"
   ]
  },
  {
   "attachments": {
    "cdb86a7c-30a3-4289-8ca3-24f9ab6ff7b0.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "389d4ec6",
   "metadata": {},
   "source": [
    "![image.png](attachment:cdb86a7c-30a3-4289-8ca3-24f9ab6ff7b0.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "340f9d5d-a4d0-4111-ab13-af3ad2896202",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Sample Output Table"
   ]
  },
  {
   "attachments": {
    "ad382bbc-cf8a-44f8-a49f-c19c34164d9f.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "a2bdeeeb-c4b0-43ca-a290-1c150d5e18c5",
   "metadata": {},
   "source": [
    "![image.png](attachment:ad382bbc-cf8a-44f8-a49f-c19c34164d9f.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0242f6d5",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce393c87",
   "metadata": {},
   "source": [
    "Utilizing the HarveyTweet Dataset, we successfully identified and classified tweets into critical categories, such as public services and health-related information. Below are the results from the sample inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e08688e",
   "metadata": {
    "vscode": {
     "languageId": "tex"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "| Tweets                                                                                                                     | Predicted Class         |\n",
    "|----------------------------------------------------------------------------------------------------------------------------|-------------------------|\n",
    "| Rockport about to get hit by 130 mph winds after several building collapsed , many injured , and high school partial â€¦   | Health emergency        |\n",
    "| Rockport close to re-entering Harvey southern eye wall . Temporary reprieve from significant time in slow moving e â€¦     | Health emergency        |\n",
    "| Rockport update with HurricaneHarvey Looks like the Fairfield Inn                                                          | Health emergency        |\n",
    "| Robstown residents , if you need sandbags swing by the Aviator Ball Park at the RichardMBorchardFairgrounds . Harvey       | Public service failures |\n",
    "| Repost ãƒ» ãƒ» ãƒ» United Way is coordinating Harvey recovery efforts along the Gulf Coast . â€¦                           | Public service failures |\n",
    "| PrayForPortA - Drive around Roberts Point Park in Port Aransas , Texas . Harvey                                            | Health emergency        |\n",
    "| Port Arthur , Texas diapers babyitems 11 am tomorrow , Sunday at St . Joseph Hall , 800 9th Ave .                          | Public service failures |\n",
    "| harvey Can't get hold of our gran on Cactus Street in Fulton - can anyone local tell me what's going on there ? stormharve | Health emergency        |\n",
    "| harvey hurricaneharvey austintx walked home lol got7 lol @ Hyde Park ( Austin , Texas )                                    | Others                  |\n",
    "| harvey Pls rescue 12 day baby family at 9 Woodstone St , Houston 77024 , flooding will reach roof soon , help my fr â€¦    | Health emergency        |\n",
    "| Harvey does anyone know about the flooding conditions around Cypress Ridge High School ? ! HurricaneHarvey HarveyFloo      | Health emergency        |\n",
    "| harvey webster Condition of Nasa Rd 1 and El Camino Real                                                                   | Public service failures |"
   ]
  }
 ],
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